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A Cooperative Learning Approach for Decentralized Peer-to-Peer Energy Trading Markets and its Structural Robustness Against Cyberattacks

IEEE access, 2021, Vol.9, p.148862-148872 [Tạp chí có phản biện]

ISSN: 2169-3536 ;EISSN: 2169-3536 ;DOI: 10.1109/ACCESS.2021.3125031 ;CODEN: IAECCG

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Nhan đề:
A Cooperative Learning Approach for Decentralized Peer-to-Peer Energy Trading Markets and its Structural Robustness Against Cyberattacks
Tác giả: Nguyen, Dinh Hoa
Chủ đề: <; italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">; r -robustness ; Computer crime ; cooperative learning ; Cost function ; Costs ; cybersecurity ; interval analysis ; inverse optimization ; optimal energy management ; P2P energy trading systems ; Peer-to-peer computing ; Renewable energy sources ; resilient consensus ; Robustness
Là 1 phần của: IEEE access, 2021, Vol.9, p.148862-148872
Mô tả: Peer-to-peer (P2P) energy trading has recently emerged as a promising paradigm for integrating renewable and distributed energy resources into local energy grids with the presence of active prosumers. However, prosumers often have different preferences on energy trading price and amount. Therefore, in decentralized P2P energy markets, a negotiation between prosumers is needed to obtain a commonly satisfactory set of preferences, i.e., a market-clearing solution. To achieve that, this paper proposes a novel approach in which a decentralized inverse optimization problem is solved by prosumers to cooperatively learn to set their objective function parameters, given their preferential intervals of energy prices and amounts. As such, prosumers' parameters can be determined in specific intervals computed analytically from the lower and upper bounds of their preferential intervals, if a certain learning condition is satisfied. Next, the structural robustness of prosumer's cooperative learning against the malicious and Byzantine models of cyberattacks is studied with the weighted-mean-subsequence-reduced (WMSR) resilient consensus algorithm. A novel sufficient robustness condition is then derived. Finally, case studies are conducted on the IEEE European Low Voltage Test Feeder system to validate the effectiveness of the proposed theoretical results.
Nơi xuất bản: IEEE
Ngôn ngữ: English
Số nhận dạng: ISSN: 2169-3536
EISSN: 2169-3536
DOI: 10.1109/ACCESS.2021.3125031
CODEN: IAECCG
Nguồn: IEEE Open Access Journals
DOAJ Directory of Open Access Journals

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A Cooperative Learning Approach for Decentralized Peer-to-Peer Energy Trading Markets and its Structural Robustness Against Cyberattacks

ISSN: 2169-3536 ;EISSN: 2169-3536 ;DOI: 10.1109/ACCESS.2021.3125031 ;CODEN: IAECCG

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